{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Looking at EMNIST data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from importlib.util import find_spec\n",
    "if find_spec(\"text_recognizer\") is None:\n",
    "    import sys\n",
    "    sys.path.append('..')\n",
    "\n",
    "from text_recognizer.data.emnist import EMNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "EMNIST Dataset\nNum classes: 83\nMapping: ['<B>', '<S>', '<E>', '<P>', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', ' ', '!', '\"', '#', '&', \"'\", '(', ')', '*', '+', ',', '-', '.', '/', ':', ';', '?']\nDims: (1, 28, 28)\nTrain/val/test sizes: 260212, 65054, 53988\nBatch x stats: (torch.Size([128, 1, 28, 28]), torch.float32, tensor(0.), tensor(0.1656), tensor(0.3257), tensor(1.))\nBatch y stats: (torch.Size([128]), torch.int64, tensor(4), tensor(65))\n\n"
     ]
    }
   ],
   "source": [
    "data = EMNIST()\n",
    "data.prepare_data()\n",
    "data.setup()\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "torch.Size([128, 1, 28, 28]) torch.float32 tensor(0.) tensor(0.1656) tensor(0.3257) tensor(1.)\ntorch.Size([128]) torch.int64 tensor(4) tensor(65)\n"
     ]
    }
   ],
   "source": [
    "x, y = next(iter(data.test_dataloader()))\n",
    "print(x.shape, x.dtype, x.min(), x.mean(), x.std(), x.max())\n",
    "print(y.shape, y.dtype, y.min(), y.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(9, 9))\n",
    "for i in range(9):\n",
    "    ax = fig.add_subplot(3, 3, i + 1)\n",
    "    rand_i = np.random.randint(len(data.data_test))\n",
    "    image, label = data.data_test[rand_i]\n",
    "    ax.imshow(image.reshape(28, 28), cmap='gray')\n",
    "    ax.set_title(data.mapping[label])"
   ]
  },
  {
   "source": [
    "## Train a CNN model"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "GPU available: True, used: True\n",
      "TPU available: None, using: 0 TPU cores\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n",
      "\n",
      "  | Name      | Type     | Params\n",
      "---------------------------------------\n",
      "0 | model     | CNN      | 1.7 M \n",
      "1 | train_acc | Accuracy | 0     \n",
      "2 | val_acc   | Accuracy | 0     \n",
      "3 | test_acc  | Accuracy | 0     \n",
      "---------------------------------------\n",
      "1.7 M     Trainable params\n",
      "0         Non-trainable params\n",
      "1.7 M     Total params\n",
      "Epoch 0:  80%|███████▉  | 2033/2542 [00:17<00:04, 113.14it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  80%|████████  | 2042/2542 [00:18<00:04, 113.41it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  81%|████████  | 2065/2542 [00:18<00:04, 114.04it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  82%|████████▏ | 2088/2542 [00:18<00:03, 114.64it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  83%|████████▎ | 2111/2542 [00:18<00:03, 115.26it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  84%|████████▍ | 2134/2542 [00:18<00:03, 115.88it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  85%|████████▍ | 2157/2542 [00:18<00:03, 116.48it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  86%|████████▌ | 2180/2542 [00:18<00:03, 117.08it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  87%|████████▋ | 2203/2542 [00:18<00:02, 117.67it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  88%|████████▊ | 2226/2542 [00:18<00:02, 118.25it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  88%|████████▊ | 2249/2542 [00:18<00:02, 118.83it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  89%|████████▉ | 2272/2542 [00:19<00:02, 119.40it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  90%|█████████ | 2295/2542 [00:19<00:02, 119.97it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  91%|█████████ | 2318/2542 [00:19<00:01, 120.52it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  92%|█████████▏| 2341/2542 [00:19<00:01, 121.08it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  93%|█████████▎| 2364/2542 [00:19<00:01, 121.63it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  94%|█████████▍| 2387/2542 [00:19<00:01, 122.17it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  95%|█████████▍| 2410/2542 [00:19<00:01, 122.67it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  96%|█████████▌| 2433/2542 [00:19<00:00, 123.20it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  97%|█████████▋| 2456/2542 [00:19<00:00, 123.72it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  98%|█████████▊| 2479/2542 [00:19<00:00, 124.23it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  98%|█████████▊| 2502/2542 [00:20<00:00, 124.74it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0:  99%|█████████▉| 2525/2542 [00:20<00:00, 125.24it/s, loss=0.574, v_num=1, val_loss=4.42, val_acc=0.0156]\n",
      "Epoch 0: 100%|██████████| 2542/2542 [00:20<00:00, 125.33it/s, loss=0.574, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  80%|███████▉  | 2033/2542 [00:18<00:04, 112.80it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  81%|████████  | 2047/2542 [00:18<00:04, 113.21it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  81%|████████▏ | 2070/2542 [00:18<00:04, 113.84it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  82%|████████▏ | 2093/2542 [00:18<00:03, 114.46it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  83%|████████▎ | 2116/2542 [00:18<00:03, 115.09it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  84%|████████▍ | 2139/2542 [00:18<00:03, 115.70it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  85%|████████▌ | 2162/2542 [00:18<00:03, 116.30it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  86%|████████▌ | 2185/2542 [00:18<00:03, 116.80it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  87%|████████▋ | 2208/2542 [00:18<00:02, 117.29it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  88%|████████▊ | 2231/2542 [00:18<00:02, 117.80it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  89%|████████▊ | 2254/2542 [00:19<00:02, 118.37it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  90%|████████▉ | 2277/2542 [00:19<00:02, 118.93it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  90%|█████████ | 2300/2542 [00:19<00:02, 119.50it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  91%|█████████▏| 2323/2542 [00:19<00:01, 120.05it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  92%|█████████▏| 2346/2542 [00:19<00:01, 120.60it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  93%|█████████▎| 2369/2542 [00:19<00:01, 121.14it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  94%|█████████▍| 2392/2542 [00:19<00:01, 121.68it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  95%|█████████▌| 2415/2542 [00:19<00:01, 122.20it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  96%|█████████▌| 2438/2542 [00:19<00:00, 122.72it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  97%|█████████▋| 2461/2542 [00:19<00:00, 123.23it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  98%|█████████▊| 2484/2542 [00:20<00:00, 123.73it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1:  99%|█████████▊| 2507/2542 [00:20<00:00, 124.22it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1: 100%|█████████▉| 2530/2542 [00:20<00:00, 124.71it/s, loss=0.513, v_num=1, val_loss=0.582, val_acc=0.785]\n",
      "Epoch 1: 100%|██████████| 2542/2542 [00:20<00:00, 124.70it/s, loss=0.513, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  80%|███████▉  | 2033/2542 [00:17<00:04, 113.44it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  81%|████████  | 2047/2542 [00:17<00:04, 113.85it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  81%|████████▏ | 2070/2542 [00:18<00:04, 114.48it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  82%|████████▏ | 2093/2542 [00:18<00:03, 115.10it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  83%|████████▎ | 2116/2542 [00:18<00:03, 115.71it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  84%|████████▍ | 2139/2542 [00:18<00:03, 116.32it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  85%|████████▌ | 2162/2542 [00:18<00:03, 116.93it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  86%|████████▌ | 2185/2542 [00:18<00:03, 117.53it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  87%|████████▋ | 2208/2542 [00:18<00:02, 118.12it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  88%|████████▊ | 2231/2542 [00:18<00:02, 118.70it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  89%|████████▊ | 2254/2542 [00:18<00:02, 119.28it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  90%|████████▉ | 2277/2542 [00:18<00:02, 119.86it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  90%|█████████ | 2300/2542 [00:19<00:02, 120.43it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  91%|█████████▏| 2323/2542 [00:19<00:01, 120.99it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  92%|█████████▏| 2346/2542 [00:19<00:01, 121.54it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  93%|█████████▎| 2369/2542 [00:19<00:01, 122.09it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  94%|█████████▍| 2392/2542 [00:19<00:01, 122.64it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  95%|█████████▌| 2415/2542 [00:19<00:01, 123.18it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  96%|█████████▌| 2438/2542 [00:19<00:00, 123.71it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  97%|█████████▋| 2461/2542 [00:19<00:00, 124.24it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  98%|█████████▊| 2484/2542 [00:19<00:00, 124.75it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2:  99%|█████████▊| 2507/2542 [00:20<00:00, 125.26it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2: 100%|█████████▉| 2530/2542 [00:20<00:00, 125.76it/s, loss=0.469, v_num=1, val_loss=0.534, val_acc=0.798]\n",
      "Epoch 2: 100%|██████████| 2542/2542 [00:20<00:00, 125.78it/s, loss=0.469, v_num=1, val_loss=0.52, val_acc=0.801] \n",
      "Epoch 3:  80%|███████▉  | 2033/2542 [00:18<00:04, 111.64it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801] \n",
      "Epoch 3:  81%|████████  | 2047/2542 [00:18<00:04, 112.05it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3:  81%|████████▏ | 2070/2542 [00:18<00:04, 112.63it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3:  82%|████████▏ | 2093/2542 [00:18<00:03, 113.25it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3:  83%|████████▎ | 2116/2542 [00:18<00:03, 113.87it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3:  84%|████████▍ | 2139/2542 [00:18<00:03, 114.48it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3:  85%|████████▌ | 2162/2542 [00:18<00:03, 115.08it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3:  86%|████████▌ | 2185/2542 [00:18<00:03, 115.68it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3:  87%|████████▋ | 2208/2542 [00:18<00:02, 116.27it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3:  88%|████████▊ | 2231/2542 [00:19<00:02, 116.85it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3:  89%|████████▊ | 2254/2542 [00:19<00:02, 117.43it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3:  90%|████████▉ | 2277/2542 [00:19<00:02, 118.00it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3:  90%|█████████ | 2300/2542 [00:19<00:02, 118.56it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3:  91%|█████████▏| 2323/2542 [00:19<00:01, 119.12it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3:  92%|█████████▏| 2346/2542 [00:19<00:01, 119.66it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3:  93%|█████████▎| 2369/2542 [00:19<00:01, 120.19it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3:  94%|█████████▍| 2392/2542 [00:19<00:01, 120.73it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3:  95%|█████████▌| 2415/2542 [00:19<00:01, 121.26it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3:  96%|█████████▌| 2438/2542 [00:20<00:00, 121.78it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3:  97%|█████████▋| 2461/2542 [00:20<00:00, 122.29it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3:  98%|█████████▊| 2484/2542 [00:20<00:00, 122.80it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3:  99%|█████████▊| 2507/2542 [00:20<00:00, 123.30it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3: 100%|█████████▉| 2530/2542 [00:20<00:00, 123.80it/s, loss=0.44, v_num=1, val_loss=0.52, val_acc=0.801]\n",
      "Epoch 3: 100%|██████████| 2542/2542 [00:20<00:00, 123.81it/s, loss=0.44, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  80%|███████▉  | 2033/2542 [00:18<00:04, 112.58it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  81%|████████  | 2047/2542 [00:18<00:04, 112.99it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  81%|████████▏ | 2070/2542 [00:18<00:04, 113.62it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  82%|████████▏ | 2093/2542 [00:18<00:03, 114.24it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  83%|████████▎ | 2116/2542 [00:18<00:03, 114.86it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  84%|████████▍ | 2139/2542 [00:18<00:03, 115.47it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  85%|████████▌ | 2162/2542 [00:18<00:03, 116.07it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  86%|████████▌ | 2185/2542 [00:18<00:03, 116.67it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  87%|████████▋ | 2208/2542 [00:18<00:02, 117.21it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  88%|████████▊ | 2231/2542 [00:18<00:02, 117.79it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  89%|████████▊ | 2254/2542 [00:19<00:02, 118.36it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  90%|████████▉ | 2277/2542 [00:19<00:02, 118.93it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  90%|█████████ | 2300/2542 [00:19<00:02, 119.50it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  91%|█████████▏| 2323/2542 [00:19<00:01, 120.05it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  92%|█████████▏| 2346/2542 [00:19<00:01, 120.60it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  93%|█████████▎| 2369/2542 [00:19<00:01, 121.15it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  94%|█████████▍| 2392/2542 [00:19<00:01, 121.69it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  95%|█████████▌| 2415/2542 [00:19<00:01, 122.22it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  96%|█████████▌| 2438/2542 [00:19<00:00, 122.74it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  97%|█████████▋| 2461/2542 [00:19<00:00, 123.26it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  98%|█████████▊| 2484/2542 [00:20<00:00, 123.76it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4:  99%|█████████▊| 2507/2542 [00:20<00:00, 124.26it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4: 100%|█████████▉| 2530/2542 [00:20<00:00, 124.75it/s, loss=0.431, v_num=1, val_loss=0.511, val_acc=0.805]\n",
      "Epoch 4: 100%|██████████| 2542/2542 [00:20<00:00, 124.76it/s, loss=0.431, v_num=1, val_loss=0.509, val_acc=0.805]\n",
      "Epoch 4: 100%|██████████| 2542/2542 [00:20<00:00, 124.04it/s, loss=0.431, v_num=1, val_loss=0.509, val_acc=0.805]\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "import pytorch_lightning as pl\n",
    "from text_recognizer.models import CNN\n",
    "from text_recognizer.lit_models import BaseLitModel\n",
    "\n",
    "model = CNN(data_config=data.config())\n",
    "lit_model = BaseLitModel(model=model)\n",
    "trainer = pl.Trainer(gpus=1, max_epochs=5)\n",
    "trainer.fit(lit_model, datamodule=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "torch.Size([128, 83])\ntensor([4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n        4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n        4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n        4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n        4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n        4, 4, 4, 4, 4, 4, 4, 4]) tensor([28,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4, 28,  4,  4,  4,  4,\n         4, 28,  4,  4, 28,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4,  4, 28,  4,\n        28, 28,  4,  4,  4, 28,  4,  4,  4,  4, 28,  4,  4,  4,  4, 28, 28, 17,\n         4, 28,  4,  4, 28, 28,  4, 28,  4, 28,  4, 28, 28,  4,  4,  4,  4,  4,\n         4, 28, 28, 28, 28,  4,  4, 28,  4,  4, 28,  4,  4,  4,  4,  4, 28, 28,\n         4,  4, 10,  4,  4, 28,  4, 41,  4, 28,  4,  4,  4,  4,  4,  4,  4, 28,\n         4,  9,  4,  4, 28,  4, 17, 28,  4,  4,  4,  4,  4,  4, 28,  4,  4,  4,\n        28, 28])\n"
     ]
    }
   ],
   "source": [
    "x, y = next(iter(data.test_dataloader()))\n",
    "\n",
    "logits = model(x)  # (B, C)\n",
    "print(logits.shape)\n",
    "\n",
    "preds = logits.argmax(-1)\n",
    "\n",
    "print(y, preds)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 648x648 with 9 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "\n",
    "fig = plt.figure(figsize=(9, 9))\n",
    "for i in range(9):\n",
    "    ax = fig.add_subplot(3, 3, i + 1)\n",
    "    rand_i = np.random.randint(len(data.data_test))\n",
    "    image, label = data.data_test[rand_i]\n",
    "\n",
    "    image_for_model = image.unsqueeze(0)  # (1, 1, 28, 28)\n",
    "    logits = model(image_for_model)  # (1, C)\n",
    "    pred_ind = logits.argmax(-1)  # (1, )\n",
    "    pred_label = data.mapping[pred_ind]\n",
    "\n",
    "    ax.imshow(image.reshape(28, 28), cmap='gray')\n",
    "    ax.set_title(f'Correct: {data.mapping[label]}, Pred: {pred_label}')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.6.12 64-bit ('fsdl-text-recognizer-2021': conda)",
   "metadata": {
    "interpreter": {
     "hash": "33e3622f00012dfea2d5a118e7142b80576e0dff20f3a9864884eee8ffa104ad"
    }
   }
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.12-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}